Rina Friedberg
Research Scientist at Meta
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolRina Friedberg is a Ph.D. statistician and research scientist in San Francisco with eight years of experience applying causal inference, machine learning, fairness, and differential privacy to real-world problems. She has driven experimentation and privacy research at LinkedIn and now conducts economics-focused research at Meta, blending rigorous theory with production-minded impact. Her open-source contributions to the widely used grf generalized random forests project notably added local linear causal forest prediction, cross-validation for ridge penalties, and variance estimation—work that bridges advanced methods and practical tooling. Rina’s background spans academia and industry, from Stanford PhD research on nonparametric methods to consulting on medical ML and experimentation-driven recommender-system problems. She often explores the subtle interactions between hypothesis testing and differential privacy, bringing careful statistical thinking to high-stakes product and policy questions.
8 years of coding experience
4 years of employment as a software developer
Bachelor of Science (B.Sc.) Mathematics, Bachelor of Science (B.Sc.) Mathematics at University of ChicagoDoctor of Philosophy - PhD Statistics, Doctor of Philosophy - PhD Statistics at Stanford University
Github Skills (8)
statistics10
regression10
machine-learning10
random-forest10
causal-inference10
r10
econometrics9
python3
Programming languages (1)
Github contributions (5)
grf-labs/grf
Apr 2018 - Apr 2020
Generalized Random Forests
Role in this project:
Data Scientist
Data ScientistContributions:2 reviews, 15 commits, 29 PRs in 2 years
Contributions summary:Rina primarily focused on implementing and improving local linear regression forests within the `grf` repository, which is focused on generalized random forests for causal inference. Their commits introduced local linear prediction capabilities and cross-validation techniques for tuning the ridge penalty (lambda). The user added tests, examples, and variance estimation for local linear causal forests, contributing significantly to the development and refinement of this specific methodology.
random-forestdata-sciencemachine-learningforestscausal-inference
rinafriedberg/gbv-analytics
Jul 2018 - Aug 2022
Contributions:12 pushes, 1 branch in 4 years 1 month
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